A novel non-invasive exhaled breath biopsy for the diagnosis and screening of breast cancer

Background Early detection is critical for improving the survival of breast cancer (BC) patients. Exhaled breath testing as a non-invasive technique might help to improve BC detection. However, the breath test accuracy for BC diagnosis is unclear. Methods This multi-center cohort study consecutively recruited 5047 women from four areas of China who underwent BC screening. Breath samples were collected through standardized breath collection procedures. Volatile organic compound (VOC) markers were identified from a high-throughput breathomics analysis by the high-pressure photon ionization–time-of-flight mass spectrometry (HPPI-TOFMS). Diagnostic models were constructed using the random forest algorithm in the discovery cohort and tested in three external validation cohorts. Results A total of 465 (9.21%) participants were identified with BC. Ten optimal VOC markers were identified to distinguish the breath samples of BC patients from those of non-cancer women. A diagnostic model (BreathBC) consisting of 10 optimal VOC markers showed an area under the curve (AUC) of 0.87 in external validation cohorts. BreathBC-Plus, which combined 10 VOC markers with risk factors, achieved better performance (AUC = 0.94 in the external validation cohorts), superior to that of mammography and ultrasound. Overall, the BreathBC-Plus detection rates were 96.97% for ductal carcinoma in situ, 85.06%, 90.00%, 88.24%, and 100% for stages I, II, III, and IV BC, respectively, with a specificity of 87.70% in the external validation cohorts. Conclusions This is the largest study on breath tests to date. Considering the easy-to-perform procedure and high accuracy, these findings exemplify the potential applicability of breath tests in BC screening. Supplementary Information The online version contains supplementary material available at 10.1186/s13045-023-01459-9.

capillary. All the mass spectra were accumulated for 60 seconds. Then, the mass spectrum peaks with m/z <350 were detected and accumulated for 60 seconds via a time-to-digital converter in HPPI-TOFMS, its design and structure details were introduced in Wang's study 8 and Meng's study 9 . The noise-reducing and baseline correction were implemented for the original mass spectrogram via anti-symmetric wavelet transformation, which was achieved by the Python package pywavelets 10 . To transfer the discrete signal of mass spectra data to standard VOC features, the area of the most substantial peak in the range of [x-0.1, x+0.1) was calculated and regarded as the feature of VOC with m/z of x. Considering no signal detected for m/z<20 and m/z>320, 1500 VOCs ions data were detected from the m/z range of [20,320) with an interval of 0.2, which were used for machine learning model construction.
Considering the qualitative ability of HPPI-TOF-MS is limited, we deduced the possible chemicals of the top ten breast cancer-related VOC ions based on their m/z, peak area, and the physicochemical property of potential biomarkers. Firstly, we search for possible breath metabolic components based on the m/z value from the human breath-omics database 11 . Then, we confirmed the most portable ions based on detectable concentrations, physicochemical properties, and published breast cancer VOC biomarkers.

Model Development
A statistical-based feature selection method was executed to select valuable VOC ions and avoid model over-fitting, and VOC ions with no significant differences (p >0.05) were excluded.
As the machine learning model was constructed, the most critical VOC ions were confirmed based on the feature importance or coefficient in the machine learning model training. Peak area difference analysis was also performed on the relative density of the VOC ions between the breast cancer and control groups. The random forest (RF) algorithm was employed as the classifier for breast cancer detection. 12 The datasets of all the enrolled participants were divided into one discovery cohort and three external validation cohorts according to the areas of enrollment. The discovery dataset was randomly split as train, internal validation, and test data sets with a ratio of 5:2:3, which were used for model construction. The internal validation data set was for model validation and confirming the cut-off value to achieve the optimal sensitivity with a specificity no less than 60%. The remaining test data set was for testing with label blinding. In this study, we constructed two breast cancer detection models, BreathBC and BreathBC-Plus. BreathBC was constructed using breath VOC data. BreathBC-Plus was constructed using breath VOC data and clinical risk factors, including age, body mass index (BMI), family cancer history, personal cancer history, and menopause status.

Statistical Analysis
Descriptive statistics were reported as frequency (percentage) for categorical variables or mean with standard deviation for continuous variables. We compared the demographics of different patient groups using the student's T test for continuous variables and the chi-square test for categorical variables. The sensitivity, specificity, positive and negative predictive value, accuracy, area under the receiver operating characteristic (ROC) curve (AUC), and relative 95% confidence interval (CI) were calculated to evaluate the performance of the VOC-based breast cancer detection models. The AUCs of the models were compared with the DeLong test. 13,14 The sample sizes were decided to verify that the AUC is non-inferior to the performance of traditional diagnostic imaging in the independent validation cohorts. Two-sided p-values less than 0.05 were considered statistically significant for all analyses. All statistical analyses were performed using

Sample Size Estimation
In this study, the minimum sample size was 12 for the breast cancer patients and 82 for the controls in each independent validation cohort to effectively verify that the performance (AUCs) of the combined model (the BreathBC-Plus) is non-inferior to the performance of traditional diagnostic imaging (AUC=0.80 for mammography and ultrasound 15 ) with the power (1-Beta) of 90% and significance level (Alpha) of 0.05 using a two-sided z-test 16,17 . Thus, we finally enrolled   The peak area was then computed for each VOC ion in breath samples. Spectrum peaks of the breast cancer patients and non-cancer women showed distinct patterns between the m/z ranging from 20 to 140. Ten optimal VOC features with the most significant differences between the cases and controls were high-lighted in red lines.   Overall, the BreathBC scores were higher in patients with breast cancer than among the controls, regardless of pathology, tumor size, lymph node status, molecular subtype, personal cancer history, and menopause status (all p<0.01). * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.   When compared with traditional imaging-based screening methods, the diagnostic performance of the BreathBC was superior to that of mammography in the external validation cohorts (Table S5)      Abbreviation: VOC, volatile organic compound; SD, standard deviation.    *P value was calculated using the Delong's method.
Abbreviation: AUC, area under the curve; CI, confidence interval.